Generating an estimate of patient radiation dose from medical imaging scans

ABSTRACT

Techniques are disclosed for estimating patient radiation exposure during computerized tomography (CT) scans. More specifically, embodiments of the invention provide efficient approaches for generating a suitable patient model used to make such an estimate, to approaches for estimating patient dose by interpolating the results of multiple simulations, and to approaches for a service provider to host a dose estimation service made available to multiple CT scan providers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/315,197, filed Dec. 8, 2011, now U.S. Pat. No. 8,953,861, which is anon-provisional application of U.S. Patent Application No. 61/420,834,filed Dec. 8, 2010, which are incorporated herein by reference in theirentireties.

FIELD OF THE INVENTION

Embodiments of the invention are generally directed to approaches forestimating patient radiation exposure during computerized tomography(CT) scans.

BACKGROUND

As is known, a CT scanning system uses ionizing radiation (X-rays) togenerate images of tissues, organs, and other structures within a body.The X-ray data resulting from a CT scan may be converted into images ona computer display screen. For example, the CT scan provides acollection of data used to create a three dimensional (3D) volumecorresponding to the scanned portion of a patient's body. The 3D volumeis then sliced to create images of body tissue at small intervals alongan axis of the patient's body. Such slices may include both lateral andtransverse slices (as well as other slices) depending on the tissues orstructures being imaged.

The use of CT scans and ionizing radiation for medical imaging has grownexponentially over the past decade. And modern techniques such as CTscanning provide much more detailed and valuable diagnostic informationthan conventional X-ray imaging. Concurrently however, patients arebeing exposed to substantially larger doses of radiation. For example, atypical chest CT will expose a patient to anywhere between 100-250 timesthe dose of a conventional chest X-Ray depending on the voltage andcurrent of the CT scanning system, the protocol followed to perform theprocedure, and the size and shape of the patient being scanned.

Despite the increased use of CT scans (and resulting exposure toradiation) the amount of radiation a patient is exposed to during aprocedure, and importantly, the cumulative dose over many procedures arenot parameters are regularly tracked for a patient, and nor are theseparameters readily accessible part of the patient's medical records.This occurs in part because the amount of radiation absorbed by internalorgans and tissues cannot be measured in live patients directly as partof a CT exam, and results obtained from cadavers, while more accurate,do not correspond well to dose absorption in live tissues.

Similarly, approaches for estimating dose used currently also provideinaccurate results. For example, one approach is to rely on a limitednumber of physical imaging phantoms to represent a given patient.However, the available imaging phantoms do not adequately represent thebroad variation in people's size and weight in the population ofindividuals receiving CT scans. As a result, single point surfacemeasurements are what is currently done in the majority of cases wheredose is estimated at all. However, this leads to both poor and widelyvarying results, depending on where the single point dose is measured.More generally, surface measurements of radiation exposure do notprovide an accurate measure of actual absorption for internal tissues,organs, and structures.

SUMMARY

Embodiments provide techniques for estimating patient radiation exposureduring computerized tomography (CT) scans. One embodiment of theinvention includes a method for determining an estimate of radiationdose absorbed by an individual in receiving an imaging scan. This methodmay generally include receiving a set of parameters describing theimaging scan and an image scanning apparatus being used to perform theimaging scan and receiving a deformed imaging phantom corresponding tothe individual. This method may further include evaluating a pluralityof previously completed simulations estimating radiation doseabsorption. Upon determining, based on the evaluation, that two or moreof the simulations match the received set of parameters and receivedimaging phantom within a specified tolerance measure, estimates ofradiation dose in the two or more simulations are interpolated todetermine the estimate of radiation dose absorbed by the individual inreceiving the imaging scan.

In a particular embodiment, interpolation may be a multivariate scatterinterpolation—e.g., using Shepard's method. Upon determining that theplurality of simulations do not include at least two simulationsmatching the received set of parameters and received imaging phantomwithin the specified tolerance measure, this method may further includeperforming a simulation of the imaging scan using the deformed imagingphantom and the set of parameters, estimating, based on the simulation,amounts of radiation absorbed by the individual as a result ofperforming the imaging scan and adding the performed simulation to theplurality of simulations. The simulation may be performed as a MonteCarlo simulation.

Additional embodiments include a computer-readable storage mediumstoring an application, which, when executed on a processor, performsthe above recited method as well as a system having a processor and amemory storing an enterprise information asset management applicationprogram, which, when executed on the processor, performs the aboverecited method.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited aspects are attained andcan be understood in detail, a more particular description ofembodiments of the invention, briefly summarized above, may be had byreference to the appended drawings. Note however, the appended drawingsillustrate only typical embodiments of the invention and are thereforenot limiting of its scope, for the invention may admit to other equallyeffective embodiments.

FIG. 1 illustrates an example of a CT scanning system and relatedcomputing systems configured to provide estimates of patient radiationdose, according to one embodiment of the invention.

FIG. 2 illustrates an example an imaging system used to obtain CT scandata, according to one embodiment.

FIG. 3 illustrates an example of a dose estimation system used toestimate and track cumulative patient dose, according to one embodiment.

FIG. 4 illustrates a method for generating a suitable model forestimating patient radiation dose resulting from CT scans, according toone embodiment.

FIG. 5A illustrates an example image representing a deformable phantom,according to one embodiment.

FIG. 5B illustrates an example of a two-dimensional (2D) reference imageof a portion of a human body corresponding to the phantom shown in FIG.5A, according to one embodiment.

FIG. 6 illustrates another method for generating a suitable model forestimating radiation dose resulting from CT scans, according to oneembodiment.

FIG. 7 illustrates an example slice of a phantom superimposed over acorresponding CT slice of a patient, according to one embodiment.

FIG. 8 illustrates an example of a transverse slice of an imagingphantom superimposed over a corresponding transverse CT slice of apatient, according to one embodiment.

FIG. 9 illustrates an example of a CT image segmentation and organvolume displacement for an imaging phantom, according to one embodiment.

FIG. 10 illustrates a method for a dose estimation service to providepatient dose estimates to multiple CT scan providers, according to oneembodiment.

FIG. 11 illustrates an example computing infrastructure for a patientdose estimation service system configured to support multiple CT scanproviders, according to one embodiment.

DETAILED DESCRIPTION

Embodiments of the invention are generally directed to approaches forestimating patient radiation exposure during computerized tomography(CT) scans. More specifically, embodiments of the invention provideefficient approaches for generating a suitable patient model used tomake such an estimate, to approaches for estimating patient dose byinterpolating the results of multiple simulations, and to approaches fora service provider to host a dose estimation service made available tomultiple CT scan providers. As described in detail below, the dosemanagement system provides a single system for tracking radiation doseacross modalities and to present information to practitioners in ameaningful and easily understood format. Routine consideration ofcumulative dose in ordering diagnostic imaging tests may lead to a moreinformed decision-making process and ultimately benefit patient safetyand care.

In one embodiment, a virtual imaging phantom is generated to model agiven patient receiving a CT scan. The virtual imaging phantom may begenerated by deforming an existing mathematical phantom to better matchthe size, shape, and/or organ positions of a patient being exposed toradiation in a CT scan. Initially, a mathematical phantom may beselected based on, e.g., an age and gender of the patient. Patientspecific geometry may be achieved by deforming the selected mathematicalphantom using transformations obtained by analyzing scout imagelocalizers of that patient. Note, in this context, as understood by oneof ordinary skill in the art, a “localizer” generally refers to a 2Dimage projection of a patient (typically an anterior/posterior X-rayimage and/or a lateral X-ray image). In such an approach, the selectedmathematical phantom may have its own reference set of localizer images.The reference images for a given virtual phantom are selected to matchthe geometry, size and positioning of that phantom (e.g., arms up or atthe side) and may be selected from imaging obtained from multipleindividuals.

Image registration techniques are then used to map points in thelocalizer image of the patient to points in the reference image (orimages) associated with the virtual phantom. Doing so results in a setof transformations that can be used to deform the virtual phantom tobetter match the geometry of the patient. A similar approach involvesusing a reference set of 3D data (selected CT scans) for the phantom andusing 3D image registration techniques to map points in a CT scan of agiven patient to points in reference CT scans associated with a givenphantom.

Similarly, image segmentation may be used to identify a 3D volume withina CT scan corresponding to organs, tissues, or structures of interest ina CT scan of a patient. The 3D volume may be a bounding box, or a moreprecise 3D volume believed to represent an organ, etc. Once identified,a displacement may be determined between the position of the organ inthe phantom and the corresponding position in the patient's CT scan.Instead of working on individual image points (as in the 2D/3D imageregistration techniques) the image segmentation approach works by usinglarger 3D volumes from the CT image as data points to determine atransformation from a virtual phantom and a given patient.

In each of these cases, the resulting hybrid phantom provides a muchmore accurate mathematical representation of a particular patient to usein a dose simulation than the unmodified phantoms alone. Once thetransformations are determined, the hybrid virtual phantom may be usedto simulate a given CT procedure for the patient. For example, wellknown Monte Carlo simulation techniques have been developed forestimating organ absorbed dose for a virtual phantom. Such simulationtechniques use the virtual phantom (as transformed relative to a givenpatient), along with a number of settings related to the CT scannermodel and procedure to be performed in order to compute accurateestimates of organ absorbed dose. For example, a CT scanner may bemodeled using kVp, i.e., peak kilovoltage, X-ray generator target angle,fan angle, collimation, slice thickness, focus to axis distance, flatfilters (material and thickness), and beam shaping filters (material andgeometry). Of course, these (and other parameters) may be selected asavailable or as needed to suit the needs of a particular case.

However, estimating organ absorbed organ dose using a Monte Carlosimulation can require significant amounts of computing time, muchlonger than required to perform an actual CT scan. Given the highutilization of CT scanning systems at many imaging facilities, in caseswhere an estimate of total cumulative dose should not exceed aprescribed maximum, this delay is simply not tractable. Even in caseswhere the estimate is not used prior to performing a given procedure,unless the estimates of patient dose can be determined in relatively thesame order of time as required to perform a procedure, then maintaininga record of dose estimation for a given scanning system becomesintractable—as the simulations will simply fall further and furtherbehind the current scans being performed. This problem growsexponentially for a SaaS provider hosting a dose estimation service inthe cloud for multiple imaging facilities.

Accordingly, in one embodiment, estimates of patient dose determined fora given procedure may be generated by interpolating between two (ormore) previously completed simulations. If no “close” simulations areavailable, then the hybrid virtual phantom, CT scanner and proceduredata may be added to a queue of full Monte Carlo simulations to beperformed. Over time, a large library of simulations allows for doseestimates to be provided in real time as procedures are scheduled andpreformed. Doing so allows cumulative dose amounts for a given patientto be captured, as well as cumulative dose limits to be observed.

Further, in one embodiment, a Software as a service (SaaS) or cloudprovider model may be used to perform the dose estimates, maintain alibrary of computed simulations, as well as run the Monte Carlosimulations. In such a case, a CT scan provider may supply the SaaSprovider with the parameters of a given CT procedure. For example,client software (or even a secure web-based portal) at an imaging centermay be used to supply the SaaS provider with a selected virtual phantom,along with transforms used to create a hybrid phantom modeling aparticular individual and the equipment and protocol to be used inperforming a CT procedure. Once received, the service provider canselect the appropriate simulations from the library to interpolate andreturn an estimate of patient organ absorbed dose to the imaging center.

Importantly, the SaaS provider need not receive any actual identifyinginformation about a given individual or patient receiving a CT scan.Instead, the SaaS provider receives only information related to avirtual phantom and a CT system/procedure. As a result, the operationsof the service provider may not require compliance with a variety oflaws and/or regulations related to the privacy of personal healthinformation. Further, by providing dose estimates for multiple imagingcenters, the resulting simulation library becomes more diverse and muchmore likely to find candidates for interpolation than a simulationlibrary generated solely from scanning procedures performed by a singleimaging center. Further still, centralizing the simulation library andMonte Carlo simulations allows improvements to the phantoms, a MonteCarlo simulation engine, and interpolation techniques to be shared byall imagining centers using the cloud based service. Lastly, thisapproach leaves it to the imaging center to maintain information tyingcumulative dose to specific patients. Allowing actual patient data toremain with each individual provider. At the same time, the SaaSprovider may, of course, communicate with the imaging centers using avariety of standardized protocols for image and data exchange,including, e.g., digital Imaging and Communications in Medicine (DICOM),Picture Archiving and Communication Systems (PACS), Health Level SevenInternational (HL7) standards, ICD-9, ICD-10 diagnosis and procedurecodes, etc.

Additionally, the following description references embodiments of theinvention. However, it should be understood that the invention is notlimited to specific described embodiments. Instead, any combination ofthe following features and elements, whether related to differentembodiments or not, is contemplated to implement and practice theinvention. Furthermore, although embodiments of the invention mayachieve advantages over other possible solutions and/or over the priorart, whether or not a particular advantage is achieved by a givenembodiment is not limiting of the invention. Thus, the followingaspects, features, embodiments and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s). Likewise, reference to“the invention” shall not be construed as a generalization of anyinventive subject matter disclosed herein and shall not be considered tobe an element or limitation of the appended claims except whereexplicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus or device.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality and operation of possible implementations ofsystems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Each block of the block diagrams and/orflowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustrations can be implemented byspecial-purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

Embodiments of the invention may be provided to end users through acloud computing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of the presentinvention, a service provider may provide imaging centers with estimatesof patient dose in both predictive and reporting perspectives. Forexample, a dose estimation interface may be used to submit virtualphantom and CT data to the cloud based provider.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Further, particular embodiments of the invention described below rely ona particular example of a computed tomography CT scanning system using aclient-server architecture to provide dose estimation to a set ofimaging, It should be understood, however, that the techniques describedherein may be adapted for use with other medical imaging technologyrelying on exposing individuals to limited radiation doses as part ofthe imaging procedure (e.g., PET scans, conventional X-ray imaging, andfluoroscopy and angiography,).

FIG. 1 illustrates an example of a CT scanning environment 100 andrelated computing systems configured to provide estimates of patientradiation dose, according to one embodiment of the invention. As shown,the CT scanning environment 100 includes a CT scanning system 105, animaging system 125, and a dose estimation system 130. Additionally, thedose estimation system 130 includes a database of imaging phantoms 132and a simulation library 134.

As is known, the CT scanner 105 provides a device used to bombard asubject 120 with X-rays from an X-ray source 110. The X-rays emittedfrom X-ray source 110 pass through tissues, organs, and structures ofthe subject 120 at different rates (some of which is absorbed by suchtissues organs and structures) depending on the density and type ofmatter which the X-rays pass through. Sensors disposed with a ring 115detect the amount of radiation that passes through the subject 120. Theresulting sensor information is passed to imaging system 125. Theimaging system 125 provides a computing device configured to receive,store, and generate images from the sensor data obtained from the CTscanner.

The imaging system 125 allows an operator to perform a given CTprocedure as well as receive data obtained carrying out CT scans. Forexample, the imaging system 125 may be configured to “window,” variousbody structures, based on their ability to block X-rays emitted fromsource 110. CT scanning images (often referred to as “slices”) aretypically made relative to an axial or transverse plane, perpendicularto the long axis of the body. However, CT scanner 105 may allow theimaging data to be reformatted in various planes or as volumetric (3D)representations of structures. Once a CT scan is performed, the imagingdata generated by CT scanner 105 may be stored allowing the resultingscan images to be reviewed or evaluated in other ways. In oneembodiment, imaging data may be formatted using the well known DICOMstandard and stored in a PACS repository.

In one embodiment, the dose estimation system 130 provides a computingsystem and software applications configured to estimate an amount ofpatient absorbed dose for a given patient receiving a given CT scan.Note, such an estimate may be made in a predictive sense (i.e., beforeperforming a scan) but may be made after the fact as well.

In the predictive case, the dose estimation system 130 may provide anestimate of patient dose prior to performing a CT scan. Further, in oneembodiment, dose estimation system 130 may be configured toautomatically generate alerts based on configurable thresholds. Thecriteria for the generating an alert may use a rule engine that can takeinto account age, gender, ICD9/ICD10 encoding, and other informationabout a given patient or procedure (e.g., a specified cumulative doselimit). More generally, dose thresholds may be flexible enough toreflect any legislative, institutional, or treatment requirements fordose monitoring. In one embodiment, the resulting dose estimates may bestored as part of a patient's medical records/history maintained by animaging center, hospital, or other provider.

Further, dose thresholds may optionally be used to create an incidentreports routed to the appropriate practitioners. Incident reports mayinclude a description of a procedure and any dose estimates that exceeda rule or threshold along with any supplementary information needed toprovide context for practitioner intervention or decision making. In oneembodiment, such a report may be printed/emailed using a customizableXML template.

Imaging phantoms 132 may provide accepted mathematical models ofportions of human tissue, organs, structures, etc. For example, imagingphantoms 132 may provide a set of non-uniform rational basis spline(NURBS) used to create a three dimensional (3D) model of a human body(or portion thereof). Alternatively, the imaging phantoms may berepresented using constructive solid geometry (CSG) or othermathematical representation. Different imaging phantoms 132 may beprovided to generally model individuals based on age and gender.However, as noted above, the virtual geometry and body shape of animaging phantom selected based on just age and/or gender may (or maynot) correspond to the size, shape and organ positions of an actualperson having a CT procedure. Accordingly, in one embodiment, the doseestimation system 130 may be configured to deform a virtual phantom tobetter model a particular patient. Example embodiments for deforming avirtual imaging phantom 122 are discussed in greater detail below.

Once an imaging phantom is deformed to model a particular individual,dose estimation system 130 may perform a simulation to estimate anamount of first pass dose deposition resulting from a given CT scanningprocedure. For example, in one embodiment, a Monte Carlo simulation maybe performed using the CT scanning parameters, CT procedure parameters,and the deformed phantom to arrive at an estimation of dose. However,other simulation approaches could be used as well. The results of agiven dose estimation simulation may be stored in the simulation library134.

For example, the CT scanner may be parameterized for a simulation basedon X-ray tube current and voltage, CT Scanner mode, kVp, X-ray generatortarget angle, fan angle, collimation, slice thickness, focus to axisdistance, flat filters (material and thickness), beam shaping filters(material and geometry). While a variety of approaches may be used inthe simulation process, in one embodiment, kVp, target angle andfiltration are used to model the X-ray spectrum as described in“Computation of bremsstrahlung X-ray spectra over an energy range 15 KeVto 300 KeV,” W. J. Iles, Regne Unit. National Radiological ProtectionBoard, NRPB, 1987.

In addition, focus to axis distance determines the distance of the X-raysource to the axis of rotation and fan angle determines how widely thebeam spreads on the slice plane. Of course, these (and other parameters)may be selected as available or as needed to suit the needs of aparticular case. Typically however, energy deposition is stored perslice for each anatomical region defined in the phantom. A normalizationsimulation of a CTDlvol phantom may be performed for each CT model. Thisper-slice energy deposition information, combined with the masses foreach anatomical region is sufficient for calculating absorbed dose toeach region for a given scan region (using a sub-set of our full bodysimulation).

However, performing a Monte Carlo simulation typically requiressubstantial processing time to complete—much longer than performing theCT scan itself. Accordingly, in one embodiment, the dose estimationsystem 130 estimates dose by interpolating between two (or more)simulations in the simulation library 134. For example, a first passpatient dose may be calculated using multivariate scatter interpolationof existing simulation data. Patient dose information is refined as moreapplicable simulations are added. Similarly, new scanner models may beadded to the simulation library 134 as calibration measurements andspecifications of these scanners are obtained.

The simulation library 134 provides a database of Monte Carlo simulationresults. In one embodiment, the simulation library 134 storesinformation on the dose/energy deposition to a set of phantoms, both assupplied and as deformed for individual patients, for a collection ofsupported medical imaging scanners, e.g., CT, RF, XA imaging modalities,among others. In one embodiment, the simulation library 134 is used toprovide real time look-up and/or calculations of dose distributionsgiven acquisition parameters, patient description, and scan region.

As noted, the simulation library 134 may be augmented automatically overtime as additional Monte Carlo simulations are completed. For example,simulations to perform may be added to a queue as CT scan examinationsoccur. Priority may be given to simulations in an area with sparseexisting data points. Doing so improves the probability of identifyingsimulations to interpolate, i.e., improves the simulation “space”covered by the simulation library 134. Similarly, more simulationsavailable in simulation library 134 allow more stringent thresholds forselecting simulations to interpolate in a given case—leading to greateraccuracy in dose estimates.

Note, while shown in FIG. 1 as part of a CT scanning environment 100,the dose estimation system 130 (and phantoms 132 and library 134) may beprovided as a hosted service accessed by/from the a CT scanningenvironment 100. For example, an imaging center may use a clientinterface on the imaging system 125 (e.g., a secure web portal ordedicated client application) to interact with a hosted dose estimationprovider. An example of such an embodiment is discussed in greaterdetail below with respect to FIGS. 11 and 12.

FIG. 2 illustrates an example an imaging system 125 used to obtain CTscan data and mange estimates of patient dose, according to oneembodiment. As shown, the imaging system 125 includes, withoutlimitation, a central processing unit (CPU) 205, a CT system interface214 network interface 215, an interconnect 217, a memory 225 and storage230. The Imaging system 125 may also include an I/O device interface 210connecting I/O devices 212 (e.g., keyboard, display and mouse devices)to the imaging system 125.

CPU 205 retrieves and executes programming instructions stored in thememory 225. Similarly, the CPU 205 stores and retrieves application dataresiding in the memory 225. The interconnect 217 facilitatestransmission of programming instructions and application data betweenthe CPU 205, I/O devices interface 210, storage 230, network interface215, and memory 225. CPU 205 is included to be representative of asingle CPU, multiple CPUs, a single CPU having multiple processingcores, and the like. And the memory 225 is generally included to berepresentative of a random access memory. The storage 230 may be a diskdrive storage device. Although shown as a single unit, the storage 230may be a combination of fixed and/or removable storage devices, such asdisc drives, solid state storage devices (SSD), network attached (NAS),or a storage area-network (SAN). Further, storage 230 (or connections tostorage repositories) may conform to a variety of standards for datastorage related to health care environments (e.g., a PACS repository).

As shown, the memory 220 includes an imaging control component 222, animage storage component 224, and a dose estimation interface 226. Andthe storage 235 imaging protocols 232 and alarm thresholds 234. Theimaging control component 222 corresponds to software applications usedto perform a given CT scanning procedure—as specified by an imagingprotocol 232. The imaging protocols 232 generally specify position,time, and duration for performing a specific CT procedure using aparticular scan modality. The image storage component 224 providessoftware configured to store images and CT data derived while performinga given CT procedure or that interacts with a suitable storagerepository to store such images and data. For example, CT scan data maybe sent over a TCP/IP connection (via network interface) to/from a PACSrepository.

The dose estimation interface 226 provides software componentsconfigured to interface with the dose estimation system 130 to obtain anestimate of patient dose that may result from a particular CT procedure.As noted, in one embodiment, the dose estimation interface 226 mayinteract with systems local to the CT imaging environment. However, inan alternative embodiment, the dose estimation interface 226 mayinteract with a hosted service provider. In such a case, the interface226 may send requests for estimates of patient dose to the hostedservice provider. Further, such request may indicate an imaging phantom,transforms to that phantom, and the CT scanning equipment and protocolsbeing followed for a given imaging scan. In either case, when being usedin a predictive sense (i.e., before performing a procedure), theestimate of patient dose may be compeered against alarm thresholds andrules to determine whether any alarms should issue prior to a givenprocedure being performed (e.g., an alarm indicating that a givenprocedure will (or would be likely to) exceed a cumulative dose limitfor a given patient, organ or body part, etc.

FIG. 3 illustrates an example of a dose estimation system 130 used toestimate and track cumulative patient dose, according to one embodiment.As shown, the dose estimation system 130 includes, without limitation, acentral processing unit (CPU) 305, a network interface 315, aninterconnect 320, a memory 325 and storage 330. The dose estimationsystem 130 may also include an I/O devices interface 310 connecting I/Odevices 312 (e.g., keyboard, display and mouse devices) to the doseestimation system 130.

Like CPU 205, CPU 305 is included to be representative of a single CPU,multiple CPUs, a single CPU having multiple processing cores, etc., andthe memory 325 is generally included to be representative of a randomaccess memory. The interconnect 317 is used to transmit programminginstructions and application data between the CPU 305, I/O devicesinterface 310, storage 330, network interface 315 and memory 325. Thenetwork interface 315 is configured to transmit data via thecommunications network, e.g., to receive requests from an imaging systemfor dose estimation. Storage 330, such as a hard disk drive or solidstate (SSD) storage drive, may store non-volatile data.

As shown, the memory 320 includes a dose estimation tool 321, whichprovides a set of software components. Illustratively, the doseestimation tool 321 includes a Monte Carlo simulation component 322, asimulation selection component 324, an image registration/segmentationcomponent 326, and a dose interpolation component 328. And storage 330contains imaging phantom data 332, CT imaging protocols 334 andsimulation library 336.

The Monte Carlo simulation component 322 is configured to estimatepatient radiation dose based on a simulation using imaging phantom data322 and a particular set of CT imaging equipment and a specified imagingprotocol 334. As noted, in one embodiment, the imaging phantom data 332may be deformed or otherwise transformed to better match the physicalcharacteristics of a given patient.

The image registration/segmentation component 326 may be configured todetermine a set of transforms for deforming the imaging phantom data 332prior to performing a Monte Carlo simulation using that phantom. Forexample, the image registration/segmentation component 326 may evaluatea reference or localizer image associated with a phantom along with ascout localizer image of a patient using image registration techniques.Image registration is the process for aligning two images into a commoncoordinate system. An image registration algorithm determines a set oftransformations to set a correspondence between the two images. Once thetransforms between the scout image of the patient and a reference imageof a phantom is determined, the same transformations may be used todeform the phantom. Such deformations may scale, translate and rotatethe geometry of the virtual phantom to correspond to the patient.

In another embodiment, image segmentation is used to identify a size anda relative position of organs, tissues, and anatomical structures of apatient. In such a case, available CT scan data for a patient may besegmented to identify geometric volumes believed to correspond to anorgan (or other structure of interest). For example, in one embodiment,image segmentation may be used to identify a bounding box believed tocontain a particular organ or structure. Other segmentation approachesmay be used to provide a more definitive 3D volumetric regioncorresponding to an organ or structure. Once identified, thisinformation is used to displace the geometry of the corresponding organ(or structure of interest) in the virtual phantom.

Note, although shown as part of the dose estimation server 130, in oneembodiment, the image registration/segmentation component 326 is part ofthe imaging system 125, or otherwise part of the computinginfrastructure at an imaging facility. Doing so allows a providerhosting a dose estimation service to receive transforms for deforming agiven virtual phantom, without also receiving any information that couldbe used to identify a patient receiving a CT scan at an imagingfacility. This approach may simplify (or eliminate) certain legal orregulatory requirements associated with entities processing protectedhealth information or medical records.

After completing a Monte Carlo simulation, the resulting estimates ofpatient dose, along with the parameters supplied to the simulationcomponent 322 are stored in the simulation library 335. In turn, thedose interpolation component 328 is used to determine an estimate ofpatient dose from the simulations in the simulation library 335, withoutperforming a complete Monte Carlo simulation. To do so, the simulationselection component 324 may compare the parameters of a CT scan, theequipment used to perform the CT scan, and the imaging phantom deformedto represent a particular individual. This information is used toidentify a set of two or (or more) simulations to interpolate. While avariety of approaches may be used, in one embodiment, the selectioncomponent 324 may use a distance measure to compare the deformedphantom, the CT procedure, and CT equipment with ones in the simulationlibrary 335. In one embodiment, the top 2 (or top N) choices areselected for interpolation. Alternatively, any simulations with anoverall similarity measure within a specified threshold are selected forinterpolation. In such a case, by tuning the thresholds more, or less,simulations are used for interpolation.

Given the set of parameters describing the scanner and patient for anexamination, (kVp, target angle, gantry tilt, height, weight, etc.) thesystem allows customizable tolerances to be set for each variable (e.g.,actual kVp is within 10 kV of simulation). When searching forsimulations, only those simulations within tolerance for all givenparameters will be factored into the calculation. In one embodiment, thesimulation results may be interpolated using the known Shepard's method.The standard deviation across the set of simulation results is used as ameasure of uncertainty (e.g. for the set of 5 simulations used, absorbeddose to the breasts has a SD of 0.2 mSv and absorbed dose to the liverhas a SD of 0.15 mSv).

FIG. 4 illustrates a method 400 for generating a suitable model forestimating patient radiation dose resulting from CT scans, according toone embodiment. More specifically, method 400 illustrates an exampleembodiment where image registration techniques are used to deform avirtual phantom. As shown, the method 400 begins at step 405, where thedose estimation tool selects a virtual phantom with pre-mapped localizerimages. As noted, the virtual phantom may be selected based on the ageand gender of an individual receiving the CT scan procedure in question.At step 410, the dose estimation tool receives a scout image of theindividual for whom the dose estimation is being performed. The scoutimage provides a 2D image projection of the individual, such as ananterior/posterior and/or lateral scout image taken by the CT scanningsystem prior to performing a full CT procedure. Alternatively, the scoutimage could be a 3D volume of the individual obtained as part of a priorCT scanning procedure. At step 415, the pre-mapped localizer imagescorresponding to use to deform the selected virtual phantom areobtained. The pre-mapped images may be selected based on the relevantregions of the patient to be scanned. For example, for a patient whowill receive (or who received) a chest CT scan, the selected referenceimage may depict this region of an individual with a body geometry thatclosely matches the virtual phantom.

FIG. 5A illustrates an example image representing a deformable phantom,according to one embodiment. As shown, image 500 provides ananterior/posterior view 501 and a lateral view 502 of a virtual imagephantom. As show in views 501 and 502, the geometry of this phantomincludes a bone structure representing ribs 505, spine 515 and legs 522.Additionally, the views 501 and 502 include geometry representingorgans, including a stomach 510 and a kidney 515. The virtual phantom(as depicted in views 501 and 502 provides a rough approximation of thesize, shape, and positioning of human organs, tissues and structures.

While clearly a rough approximation of actual human anatomy, virtualphantoms are generally accepted as providing reasonably accurateestimates of dose absorption. FIG. 5B illustrates an example of a 2Dreference image of a portion of a human body corresponding to thephantom shown in FIG. 5A, according to one embodiment. As shown, therelative positions, size, shape of the bones, tissues, organs, in thereference image match well to the corresponding positions in the virtualphantom.

Referring again to the method 400, at step 420, the dose estimation toolperforms an image registration process to determine a transformationbetween the scout images of the patient and the reference images used torepresent the virtual phantom. The result of the image registration is amapping from points in the 2D scout localizer to points in the referenceimage (or vice-versa). Similarly, in cases of a 3D scout image of thepatient (i.e., a current or prior CT scan), 3D image registrationtechniques may map points between the 3D scout image of the patient andpoints in a reference image corresponding to the phantom in a 3Dcoordinate space.

At step 425, this same transformation is used to deform the geometryrepresenting the virtual phantom. By deforming the virtual phantom usingtransformations obtained from the image registration process, the size,shape, and organ positions represented by the geometry of the virtualphantom matches the geometry of the actual patient much more accurately.For example, performing an image registration process using thereference image shown in 5B and a scout localizer of a patient providesa transformation can be used to deform the virtual phantom shown in FIG.5A. The deformed virtual phantom may be used to estimate organ absorbeddose resulting from a given CT procedure (either before or after such aprocedure is performed). That is, the dose estimations obtained from aMonte Carlo simulation are tailored to the patient, as well as moreaccurate and more consistent when used to estimate patient dose overmultiple scans.

FIG. 6 illustrates another method for generating a suitable model forestimating radiation dose resulting from CT scans, according to oneembodiment. More specifically, method 600 illustrates an exampleembodiment where image segmentation techniques are used to deform avirtual phantom. Like method 400, method 600 begins where the doseestimation tool selects an imaging phantom to deform, e.g., based on anage and gender of a patient (step 605). However, instead of retrieving2D image localizers of the patient, the dose estimation tool receives a3D scan volume of some portion of the patient (at step 610), e.g., a CTscan from a prior chest and abdomen CT. Once obtained, imagesegmentation is used to identify tissues, organs, structures, or otherlandmarks in the image volume (step 615). While a variety of availablesegmentation approaches can be used, in one embodiment, the imagesegmentation provides a minimal bounding box surrounding each identifiedorgan or structure.

At step 620, the dose estimation tool matches the organs and otheranatomical landmarks (e.g., bone position) identified in the CT scansegmentation with corresponding landmarks in the virtual phantom. Forexample, FIG. 7 illustrates an example slice of a CT scan superimposedover a corresponding slice of a virtual phantom, according to oneembodiment. In this example, the virtual phantom slice 700 includes aline 702 representing the volume bounded by the phantom along with sliceportions of a heart 701, lung 703, spine 704, and humerus bone 705.However, the location and position of the heart and lung organs in thevirtual phantom do not correspond well with the position of these organsas depicted in the CT. For example, the open space region of the lungs(at 706) does not match the size or position of lungs 702 organs in thephantom. Similarly, the boundary line 702 of the phantom does notcorrespond well with the patient. Using this phantom to estimate dose,therefore, results in much greater dose absorption than would actuallyoccur, because the phantom does not account for the large amounts ofadipose tissues in this patient.

At the same time, other landmarks of the phantom line up well with thepatient. For example, the spine and arms are generally collocated inboth the phantom (spine 704 humerus 705) and in the CT. Accordingly, atstep 625, the dose estimation system, determines a 3D displacement mapbased on the matched anatomical or structural landmarks.

For example, in FIG. 7, phantom slice 700 shows an unmodified orun-deformed phantom and phantom slice 710 the same phantom slice afterbeing displaced using the method of FIG. 6 (or after being deformedusing an image registration technique according to the method of FIG.4).

As shown in phantom slice 710, after being deformed using the identifiedorgan volumes and displacement of a particular patient the boundary line702′ now more closely follows the contours of the patient CT scan, andthe lungs 703′ and heart 701′ of the phantom have been displace tobetter reflect the position of these organs in the scan. At the sametime, other anatomical landmarks such as the spine and humerus boneremain in the same general position. The imaging phantom shown in slice700 is shown superimposed over the corresponding CT scan slice of apatient in slice 720. Similarly, the deformed phantom shown in slice 710is shown superimposed over the corresponding CT scan slice of a patientin slice 730.

Referring again to FIG. 6, at step 630, the dose estimation toolgenerates a rasterized 3D representation of the displaced organs,tissues, and structures of the virtual phantom. As noted, above, thevirtual phantom may be described as a series of non-uniform rationalbasis splines (NURBS), while the CT scan data is typically representedas a series of 3D coordinate single point values referred to as a“voxels”-short for “volume element,” a voxel extends the concept of apixel into a third dimension, and a variety of known approaches areavailable for “voxelizing” a collection of NURBs or CSG data. Doing soconverts the geometric or mathematical representation of NURBs or CSGdata into a 3D array of voxel values. In one embodiment, step 630 (thevoxelization step) is performed in order to avoid discontinuities thatoften are a problem with Mote Carlo simulations in mathematical phantoms(whether NURB or CSG based). Further, voxel based models are well-suitedto GPU-based computational methods to achieve improved speed.

Once the rasterized phantom is generated, it may be used to estimateorgan absorbed dose resulting from a given CT procedure (either beforeor after such a procedure is performed). Like the image segmentationapproaches, dose estimations performed using the phantom deformed usingthe segmentation approach are tailored to the patient, resulting in moreaccurate and more consistent dose estimates, both for individual andmultiple scans.

FIG. 8 illustrates an example of a transverse slice of an imagingphantom superimposed over a corresponding transverse CT slice of apatient, according to one embodiment. In this example, a transverse view800 corresponds to view 710 of FIG. 7 and a transverse view 850corresponds to view 730 of FIG. 7. The transverse view is created bycompositing a linear section of individual slices to create alongitudinal image. As shown, transverse views 800 and 805 provide afull length view including components not present in the superimposed CTimage of the patient, e.g., brain 801 and kidney 802. As shown in view800, a boundary 810 of the virtual phantom does not correspond well withthe outline of the patient (i.e., with the body size body size boundedby the patient's skin). However, in view 850, a boundary 815 of thephantom has been displaced to better match the reference CT scan data ofthis patient. Similarly, internal organs, structures and other tissuesmay be displaced as well.

Importantly, this example illustrates that displacement may occur forelements of the virtual phantom that are not part of the CT scan data ofthe patient. For example, the kidney 802 could be displaced by themovement of other organs for which CT scan data is available, as shownby the displaced position of kidney 802′ in view 850. Further, thisexample illustrates that a virtual phantom is required to estimatepatient dose even where CT scan data is available. This occurs asalthough the CT scan in this example was limited to the chest andabdomen, X-ray scatter will result in some absorption by the brain,kidneys, and other organs and tissues of this patient. Stateddifferently, the virtual phantom is required to estimate organ doseabsorption for organs not imaged as part of a given CT scan orprocedure.

FIG. 9 illustrates another example of a CT image segmentation and organvolume displacement for an imaging phantom, according to one embodiment.In this example, a CT volume 900 corresponding to an imagining includesa set of bounding boxes representing a segmented image position for avariety of organs, e.g., liver 905, gall bladder 910, and right adrenalgland 915. Additionally, volume 900 shows arrows representing thedisplacement of these organs based on an image segmentation of CT scandata. In this particular example, the liver 905 has been displaced downand to the right, while gall bladder 910 has been displaced up and tothe front of the liver 905 and right adrenal 915 has moved up and to theleft into the space formerly occupied by the liver 905. Further, in thisexample, the organs are represented by bounding boxes, and are displacedbased a geometric centroid. However, in an alternative embodiment imagesegmentation (for either the phantom or the CT image data of a patient)may provide a more accurate geometric volume representing an element oforgan tissue or body structure. In such a case, the displacement couldbe based on a mass centroid of the organ, e.g., where the centroid ofthe liver is localized to one side based on mass or other approach thataccounts for the topology of a given organ volume.

As illustrated in this example, displacing one organ (e.g., the liver905) in a phantom based on its corresponding position in a CT referencescan, may require displacing other organs (e.g., the gall bladder 910and right adrenal 915) as a result. This occurs as two organs plainlyshould not occupy the same physical volume when the phantom is used toperform a dose estimate analysis. Accordingly, in one embodiment, thedose estimation tool may displace organs, tissues or structures untilreaching a “steady state.”

Note, the example embodiments illustrated in FIGS. 4 and 6 may be usedseparately or in conjunction with one another to deform a virtualphantom. The particular approach or combination of approaches selectedmay be tailored to suit the needs in a particular case based on theavailable imaging phantoms, mapped 2D and/or 3D reference images, aswell as on the availability and type of localizer scout images and/orprior CT scan data for a given patient.

In one embodiment, a cloud provider model host systems used to performthe dose estimates, maintain a library of computed simulations, as wellas run the Monte Carlo simulations to augment the simulation librarywith new cases. For example, FIG. 10 illustrates a method 1000 for adose estimation service to provide patient dose estimates to multiple CTscan providers.

As shown, the method 1000 begins at step 1005 where the dose estimationservice receives an image phantom (or a reference to an image phantom)along with 2D or 3D image registration transforms or 3D volumetricdisplacement field and phantom voxelization. In an alternativeembodiment, the dose estimation service may receive data describing thedeformed phantom such as the transformed NURBS resulting from the 2D or3D image registration process or CT field displacement techniquesdescribed above.

At step 1010, the dose estimation services receives parameters of a CTscanning system and an imaging plan for a CT scan performed (or to beperformed) on a patient. Once the parameters of the patient, scanningequipment, and CT scan provider are received, the dose estimationservice may identify two (or more) simulations in the library matchingthe transformed phantom, CT scanning system parameters and imaging plan(step 1015). The provider can set customizable tolerances to be set foreach variable (e.g., actual kVp is within 10 kV of simulation). Furtherevaluating simulations, only simulations within tolerance for all (orspecied set) of the given parameters are factored into the calculation.In one embodiment, the simulation results may be interpolated using theknown Shepard's method. The standard deviation across the set ofsimulation results is used as a measure of uncertainty (e.g. for the setof 5 simulations used, absorbed dose to the breasts has a SD of 0.2 mSvand absorbed dose to the liver has a SD of 0.15 mSv).

At step 1020, the dose estimation service determines whether thematching simulations identified at step 1015 are within a toleranceparameter (or meets other thresholds or criteria). If not, then theimage phantom (and deformations/transformations) and received parametersare added to a queue of patient/scanner/image plan scenarios to simulate(step 1025). As noted, the simulation may use Monte Carlo simulationtechniques to determine estimates of organ absorbed dose tailored toboth the individual patient (based on the deformed phantom and theparticular imaging facility based on the CT scanner andcalibration/setting data.

However, as the simulation library of a SaaS provider grows, mostrequests should identify a set of simulations to interpolate. At step1030, the dose estimation service performs a multivariate scatterinterpolation using the matching simulations identified at step 1015 toestimate organ absorbed dose for a particular patient and associated CTscanning procedure. Note, such an analysis may be performed much morequickly than a full Monte Carlo simulation, allowing dose estimates tokeep pace with a sequence of procedures performed at a given imagingfacility (or facilities) as well as being provided concurrent with agiven procedure (e.g., to ensure cumulative dose limits are notexceeded. In one embodiment, the multivariate scatter interpolationmethod currently used is referred to as ‘Shepard's method’. Examples ofthis method are described in Shepard, Donald (1968). “A two-dimensionalinterpolation function for irregularly-spaced data”. Proceedings of the1968 ACM National Conference. pp. 517-524.

At step 1035, once the interpolation process is complete, dose estimatesare returned to a requesting system (e.g., a dose estimate clientprogram running on a computing system at an imaging facility). At theclient a dose management system tracks patient organ equivalent dose,effective dose, CTDI, DLP, DAP down to the examination level. Thisinformation is also summed up to provide cumulative tracking of organequivalent dose, effective dose, CTDI, DLP, DAP for a given patient'shistory. Further aggregation of this information is used to provideinstitution-wide presentation of per capita organ equivalent dose,patient effective dose, CTDI, DLP, DAP. Thus, the dose estimationservice may provide an imaging facility with a broad variety of. Thissame information is available to an imaging facility that runs a localinstance of the dose estimation system.

FIG. 11 illustrates an example computing infrastructure 1100 for apatient dose estimation service system configured to support multiple CTscan providers, according to one embodiment. As shown, a cloud basedprovider 1125 hosting a dose estimation service 1130 receives requestsfor dose estimates over network 1120 from imaging facilities 1105 ₁₋₂.At each imaging facility 1105, a CT system 1110 is used to provideimaging services for patients. An imaging/dose client 1115 communicateswith the dose estimation service 1130 to request and receive estimatesof patient dose, where the dose estimates are tailored based on theprocedure and patient. As noted, the request may include parameters fora CT procedure, scanning equipment and modality, and a deformed phantom(or transformations used to deform a phantom) based on the bodymorphology of the particular patient.

At the dose estimation service 1130, a simulation library 1135 is usedto select simulations for interpolating an amount of patient dose usingdata in the request and modules of a CT scanner and procedures (shown inFIG. 11 a phantom/CT system data 1140). If no good candidate simulationsare available for interpolation, then the service 1130 may add therequest to a queue of simulations to perform. Monte Carlo simulationsare then performed in response to the request, providing both anestimate of patient dose for a given patient and imaging procedure aswell as a new simulation data point to add to the library 1125.

Advantageously, embodiments of the invention provide a variety oftechniques for estimating radiation doses that result from CT (andother) X-ray imaging techniques. As described, image registrationtechniques and/or image segmentation techniques may be used to create ahybrid imaging phantom that more accurately matches an individual's bodysize shape. Doing so improves the accuracy of dose estimates determinedfrom a simulation. That is, the resulting hybrid phantom provides a muchmore accurate mathematical representation of a particular patient to usein a dose simulation than the unmodified phantoms alone.

Once the transformations are determined, the hybrid virtual phantom maybe used to simulate a given CT procedure for the patient. For example,Monte Carlo simulation techniques may be used to estimate organ absorbeddose for a virtual phantom. Such simulation techniques use the virtualphantom (as transformed relative to a given patient), along with anumber of parameters related to the CT scanner model and procedure to beperformed in order to compute accurate estimates of organ absorbed dose.However, estimating organ absorbed organ dose using a Monte Carosimulation can require significant amounts of computing time, muchlonger than required to perform an actual CT scan. Accordingly, in oneembodiment, estimates of patient dose determined for a given proceduremay be generated by interpolating between two (or more) previouslycompleted simulations. If no “close” simulations are available, then thehybrid virtual phantom, CT scanner and procedure data may be added to aqueue of full Monte Carlo simulations to be performed. Over time, alarge library of simulations allows for dose estimates to be provided inreal time as procedures are scheduled and performed. Doing so allowscumulative dose amounts for a given patient to be captured, as well ascumulative dose limits to be observed. Further, in one embodiment, aSaaS provider is a hosted dose estimation service provided to multipleimaging facilities. In such a case, the service provider may have arobust library of simulations to use in interpreting dose estimates forthe imaging providers.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A computer-implemented method for determining anestimate of radiation dose absorbed by an individual in receiving animaging scan, the method comprising: receiving a set of parametersdescribing the imaging scan and an image scanning apparatus being usedto perform the imaging scan; receiving a deformed imaging phantomcorresponding to the individual; evaluating a plurality of previouslycompleted simulations each estimating radiation dose absorption, whereinthe plurality of previously completed simulations are selected from asimulation library; and determining, based on the evaluation, whether toperform a simulation of the imaging scan using the received set ofparameters and the deformed imaging phantom to determine the estimate ofradiation dose absorbed by the individual, wherein the simulation of theimaging scan, when performed, is stored in the simulation library tothereby increase a probability of identifying, for subsequent imagingscans, suitable previously completed simulations for estimatingradiation dose without performing simulations of the subsequent imagingscans.
 2. The method of claim 1, wherein determining whether to performthe simulation of the imaging scan is based on a measure of similarityof the plurality of previously completed simulations to the received setof parameters and to the received deformed imaging phantom.
 3. Themethod of claim 2, wherein two or more previously completed simulationsof the plurality of previously completed simulations match the receivedset of parameters and the received deformed imaging phantom within aspecified tolerance measure, the method further comprising:interpolating the estimates of radiation dose in the two or morepreviously completed simulations to determine the estimate of radiationdose absorbed by the individual in receiving the imaging scan.
 4. Themethod of claim 1, wherein the simulation of the imaging scan isdetermined to be performed based on meeting a threshold level ofdissimilarity between the received set of parameters and the receiveddeformed imaging phantom, and the plurality of previously completedsimulations.
 5. The method of claim 1, wherein the simulation of theimaging scan is added to a queue of other simulations to be performed,and wherein the simulation of the imaging scan is given priority over atleast one of the other simulations to be performed because of theincreased probability of identifying suitable previously completedsimulations for estimating radiation dose in subsequent imaging scans.6. The method of claim 1, wherein the simulation of the imaging scan isa Monte Carlo simulation.
 7. A non-transitory computer-readable storagemedium comprising computer-readable code that, when executed by aprocessor, performs an operation for determining an estimate ofradiation dose absorbed by an individual in receiving an imaging scan,the operation comprising: receiving a set of parameters describing theimaging scan and an image scanning apparatus being used to perform theimaging scan; receiving a deformed imaging phantom corresponding to theindividual; evaluating a plurality of previously completed simulationseach estimating radiation dose absorption, wherein the plurality ofpreviously completed simulations are selected from a simulation library;and determining, based on the evaluation, whether to perform asimulation of the imaging scan using the received set of parameters andthe deformed imaging phantom to determine the estimate of radiation doseabsorbed by the individual, wherein the simulation of the imaging scan,when performed, is stored in the simulation library to thereby increasea probability of identifying, for subsequent imaging scans, suitablepreviously completed simulations for estimating radiation dose withoutperforming simulations of the subsequent imaging scans.
 8. Thecomputer-readable storage medium of claim 7, wherein determining whetherto perform the simulation of the imaging scan is based on a measure ofsimilarity of the plurality of previously completed simulations to thereceived set of parameters and to the received deformed imaging phantom.9. The computer-readable storage medium of claim 8, wherein two or morepreviously completed simulations of the plurality of previouslycompleted simulations match the received set of parameters and thereceived deformed imaging phantom within a specified tolerance measure,the method further comprising: interpolating the estimates of radiationdose in the two or more previously completed simulations to determinethe estimate of radiation dose absorbed by the individual in receivingthe imaging scan.
 10. The computer-readable storage medium of claim 7,wherein the simulation of the imaging scan is determined to be performedbased on meeting a threshold level of dissimilarity between the receivedset of parameters and the received deformed imaging phantom, and theplurality of previously completed simulations.
 11. The computer-readablestorage medium of claim 7, wherein the simulation of the imaging scan isadded to a queue of other simulations to be performed, and wherein thesimulation of the imaging scan is given priority over at least one ofthe other simulations to be performed because of the increasedprobability of identifying suitable previously completed simulations forestimating radiation dose in subsequent imaging scans.
 12. Thecomputer-readable storage medium of claim 7, wherein the simulation ofthe imaging scan is a Monte Carlo simulation.
 13. A system, comprising:a processor; and a memory storing computer-readable code configured toperform an operation for determining an estimate of radiation doseabsorbed by an individual in receiving an imaging scan, the operationcomprising: receiving a set of parameters describing the imaging scanand an image scanning apparatus being used to perform the imaging scan;receiving a deformed imaging phantom corresponding to the individual;evaluating a plurality of previously completed simulations eachestimating radiation dose absorption, wherein the plurality ofpreviously completed simulations are selected from a simulation library;and determining, based on the evaluation, whether to perform asimulation of the imaging scan using the received set of parameters andthe deformed imaging phantom to determine the estimate of radiation doseabsorbed by the individual, wherein the simulation of the imaging scan,when performed, is stored in the simulation library to thereby increasea probability of identifying, for subsequent imaging scans, suitablepreviously completed simulations for estimating radiation dose withoutperforming simulations of the subsequent imaging scans.
 14. The systemof claim 13, wherein determining whether to perform the simulation ofthe imaging scan is based on a measure of similarity of the plurality ofpreviously completed simulations to the received set of parameters andto the received deformed imaging phantom.
 15. The system of claim 14,wherein two or more previously completed simulations of the plurality ofpreviously completed simulations match the received set of parametersand the received deformed imaging phantom within a specified tolerancemeasure, the method further comprising: interpolating the estimates ofradiation dose in the two or more previously completed simulations todetermine the estimate of radiation dose absorbed by the individual inreceiving the imaging scan.
 16. The system of claim 13, wherein thesimulation of the imaging scan is determined to be performed based onmeeting a threshold level of dissimilarity between the received set ofparameters and the received deformed imaging phantom, and the pluralityof previously completed simulations.
 17. The system of claim 13, whereinthe simulation of the imaging scan is added to a queue of othersimulations to be performed, and wherein the simulation of the imagingscan is given priority over at least one of the other simulations to beperformed because of the increased probability of identifying suitablepreviously completed simulations for estimating radiation dose insubsequent imaging scans.
 18. The system of claim 13, wherein thesimulation of the imaging scan is a Monte Carlo simulation.